Kinematic and Kinetic Comparisons of Arm and Hand Reaching

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Kinematic and Kinetic Comparisons of Arm andHand Reaching Movements with Mild andModerate Gravity-Supported, Computer-Enhanced Armeo®spring: A Case StudyQussai M. ObiedatUniversity of Wisconsin-Milwaukee

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KINEMATIC AND KINETIC COMPARISONS OF ARM AND HAND REACHING

MOVEMENTS WITH MILD AND MODERATE GRAVITY-SUPPORTED,

COMPUTER-ENHANCED ARMEO®

SPRING: A CASE STUDY

by

Qussai M. Obiedat

A Thesis Submitted in

Partial Fulfillment of the

Requirements for the Degree of

Master of Science

in Occupational Therapy

at

The University of Wisconsin-Milwaukee

May 2013

ii

ABSTRACT

KINEMATIC AND KINETIC COMPARISONS OF ARM AND HAND REACHING

MOVEMENTS WITH MILD AND MODERATE GRAVITY-SUPPORTED,

COMPUTER-ENHANCED ARMEO®

SPRING: A CASE STUDY

by

Qussai M. Obiedat

The University of Wisconsin-Milwaukee, 2013

Under the Supervision of Professor Ying-Chih Wang

Background: Stroke has been recognized as a leading cause of serious long-term

disability in the United States (U.S.) with 795,000 people experience a new or recurrent

stroke each year (Roger et al., 2011). The most apparent defect after stroke is motor

impairments (Masiero, Armani, & Rosati, 2011). Statistically, half of stroke survivors

suffer from upper extremity hemiparesis and approximately one quarter become

dependent in activities of daily living (Sanchez et al., 2006). There is strong evidence that

intensity and task specificity are the main drivers in an effective treatment program after

stroke. In addition, this training should be repetitive, functional, meaningful, and

challenging for a patient (Van Peppen et al., 2004). The use of robotic systems to

complement standard poststroke multidisciplinary programs is a recent approach that

looks very promising. Robotic devices can provide high-intensity, repetitive, task-

specific, interactive treatment of the impaired limb and can monitor patients' motor

progress objectively and reliably, measuring changes in quantitative movement

kinematics and forces (Masiero, Armani, & Rosati, 2011).

iii

Objective: The purpose of this study was to examine the role of Armeo®Spring

(Hocoma, Inc.), a gravity-supported, computer-enhanced robotic devise, on reaching

movements while using two different gravity-support levels (mild and moderate weight

support) on individuals with stroke.

Methods: One stroke subject and one gender-matched healthy control

participated in this study after gaining their informed consent. Both subjects performed a

computer-based game (picking apples successfully and placing them in a shopping cart)

under two gravity weight-support conditions (mild and moderate) provided by the

Armeo®Spring device. The game tasks were described as a reaching cycle which

consisted of five phases (initiation, reaching, grasping, transporting, and releasing). Joint

angles for the glenohumeral and elbow joints throughout the reaching cycle were found.

Three kinematic parameters (completion time, moving velocity, acceleration) and one

kinetic parameter (vertical force acting on the forearm) was calculated for various

instances and phases of the reaching motion. In addition, the muscle activation patterns

for anterior deltoid, middle deltoid, biceps, triceps, extensor digitorum, flexor digitorum,

and brachioradialis were found and the mean magnitude of the electromyography (EMG)

signal during each phase of the reaching cycle was found as a percentage of the subject’s

maximum voluntary contraction (MVC).

Results: Within the healthy control subject, results demonstrated no

significant differences in mean completion time, moving velocity, or acceleration

between mild to moderate gravity-support levels during all phases of the cycle. The

stroke subject results revealed a significant decrease in the cycle mean completion time

(p= 0.042) between the two gravity-support levels, specifically in mean completion time

iv

of the grasping phase. A significant increase was found in the initiation phase moving

velocity (p=0.039) and a significant decrease was found in the grasping phase (p=0.048)

between two gravity-support levels in the stroke subject. Between subjects, significant

increase in the cycle mean completion time was found under both mild and moderate

conditions (p<.001 for both conditions). Additionally, significant decreases in the moving

velocities were found in all phases of the cycle between the healthy control and the stroke

subject under both conditions. With increasing weight support, the healthy control subject

showed an increase in abduction and flexion degrees at the glenohumeral joint level, and

an increase in flexion degrees of the elbow joint. On the other hand, the stroke subject

showed a decrease in abduction degrees and an increase in flexion degrees at the

glenohumeral joint level, and a decrease in flexion degrees of the elbow joint after

increasing the weight-support level. Results demonstrated an increase in the mean of

vertical forces when changing gravity-support levels from mild to moderate during all

phases of the cycle in both stroke and healthy subjects. Last, the average EMG magnitude

during the reaching cycle phases was reduced for muscles acting against gravity (anterior

deltoid, middle deltoid, biceps, and brachioradialis) in both the healthy control and the

stroke subject.

Conclusion: The significant differences in movement performance between mild

and moderate physical weight support suggested a preliminary result that the gravity-

supported mechanism provides a mean to facilitate functional upper limb motor

performance in individuals with stroke. Future studies should examine such effects with

larger sample sizes.

v

TABLE OF CONTENTS

Introduction ................................................................................................................... 1

Significance of the Study .................................................................................. 3

Background and Literature Review .............................................................................. 4

Types of Stroke ................................................................................................. 4

Ischemic Stroke ..................................................................................... 4

Hemorrhagic Stroke .............................................................................. 6

Transient Ischemic Attack .................................................................... 7

Symptoms & Complications ............................................................................. 7

Motor Recovery ................................................................................................ 9

Robot-assisted Therapy ..................................................................................... 12

Gravity Compensation ...................................................................................... 14

Reaching Studies ...............................................................................................15

Specific Aims and Hypothesis ...................................................................................... 16

Methods......................................................................................................................... 17

Participants ........................................................................................................ 17

Inclusion and exclusion criteria ........................................................................ 17

Device: ArmeoSpring ....................................................................................... 18

Armeo®Spring Weight-Support System .......................................................... 20

Game: Fruit Shopping ....................................................................................... 21

Data Collection ................................................................................................. 22

Procedure .......................................................................................................... 23

MVC Procedure ................................................................................................ 24

Kinematic Model .............................................................................................. 24

Data Analysis .................................................................................................... 27

vi

Results ........................................................................................................................... 28

Kinematic parameters ....................................................................................... 29

Joint Angles ......................................................................................................32

Forearm Vertical Forces ................................................................................... 32

Electromyography (EMG) ................................................................................ 33

Discussion .....................................................................................................................38

References ..................................................................................................................... 42

Appendix A: Previous Reaching Studies in the Literature ........................................... 52

Appendix B: Text Descriptions .................................................................................... 59

vii

LIST OF FIGURES

Figure 1. The Armeo®Spring study setup .....................................................................19

Figure 2. Armeo®Spring weight support system ..........................................................20

Figure 3. The print screen of the fruit shopping game ..................................................22

Figure 4. The mean completion time between the two gravity-support levels .............30

Figure 5. Joint angle changes during the reaching cycle for the healthy subject

(upper panel) and the stroke subject (lower panel) .......................................................33

viii

LIST OF TABLES

Table 1. Brunnstrom and Twitechell motor recovery stages ........................................11

Table 2: Armeo®Spring support levels ........................................................................21

Table 3. Markers used in the motion caption procedure ...............................................24

Table 4. MVC testing positions ....................................................................................26

Table 5. Anatomical coordinate systems ......................................................................26

Table 6. Armeo®Spring mild and moderate weight-support levels for stroke

and healthy subjects ......................................................................................................29

Table 7. Kinematic parameters of the healthy subject with mild & moderate

weight support ...............................................................................................................30

Table 8. Kinematic parameters of the stroke subject with mild & moderate

weight support ...............................................................................................................31

Table 9. Vertical support forces for healthy control and stroke subject with

mild & moderate weight support ..................................................................................35

Table 10.Healthy subject EMG average magnitude (% of MVC) ................................35

Table 11. Stroke subject EMG average magnitude (% of MVC) .................................36

Table 12. Healthy subject EMG minimum magnitude (% of MVC) ............................36

Table 13. Stroke subject EMG minimum magnitude (% of MVC) ..............................37

Table 14. Healthy subject EMG maximum magnitude (% of MVC) ...........................37

ix

Table 15. Stroke subject EMG maximum magnitude (% of MVC) .............................38

Table 16. P-values for between-subjects average EMG magnitude .............................38

1

Introduction

Recently, stroke has been recognized as one of the leading causes of serious long-

term disability in the United States (U.S.). Approximately 795,000 people experience a

new or recurrent stroke each year (Roger et al., 2011). Although the medical treatment

improvements of the complications caused by stroke decreased the mortality rate of the

disease, 90% of the survivors still suffer from significant neurological deficits (Volpe,

Krebs, & Hogan, 2001). The most common defects after stroke are upper extremity

functional impairments and disability in activities of daily living (Masiero, Armani, &

Rosati, 2011). Statistically, half of stroke survivors suffer from upper extremity

impairments and approximately one quarter become dependent in activities of daily

living (Sanchez et al., 2006).

Loureiro, Harwin, Nagai, and Johnson (2011) categorized current, available

upper extremity stroke rehabilitation methodologies and technologies as: conventional

physical and occupational therapy, constraint-induced movement therapy, and robotic-

aided and sensor-based therapy systems. Although an increased effort has been made to

enhance the recovery process following stroke, patients generally do not reach their full

recovery potential when discharged from hospital following initial rehabilitation. This

can be attributed to the economic pressure and the lack of available human resources

(Loureiro, Harwin, Nagai, & Johnson, 2011). These facts lead to focus more on robot-

assisted therapy as an equivalent in quality to traditional methods. The use of robot

assisted therapy will deliver therapy at reduced cost and provide a solution to overcome

the labor-intensive, one-to-one stroke rehabilitation.

2

The development, preliminary clinical use, and effectiveness of the

Armeo®Spring, a gravity-supported, computer-enhanced robotic devise, for individuals

with upper limb motor dysfunction have been supported (Gijbels et al., 2011; Housman,

Scott, & Reinkensmeyer, 2009; Sanchez et al., 2006). A study conducted by Sanchez et

al., (2006) demonstrated that individuals with chronic stroke whose arm function is

compromised in a normal gravity environment could perform reaching and drawing

movements while using T-WREX (the prototype version of the Armeo®Spring). The

patients improved their motor function (mean change in Fugl-Meyer score was 5 points)

over a period of eight weeks. Results from Housman, Scott, & Reinkensmeyer, (2009)

showed that, using the T-WREX can improve arm movement ability after chronic severe

hemiparesis with brief one-on-one assistance from a therapist (approximately 4 minutes

per session). Additionally, the 3-dimensional weight support, instant visual movement

feedback, and simple virtual reality software provided by T-WREX were associated with

modest sustained gains at 6-month follow-up (mean change in Fugl-Meyer score was 3.6

points) when compared with the conventional approach (mean change in Fugl-Meyer

score was 1.5 points). The study conducted by Gijbels et al., (2011) was in multiple

sclerosis (MS) and thus results were not described here.

The fundamental kinematic and kinetic comparisons of arm and hand reaching

movements with gravity-supported, computer-enhanced Armeo®spring have not been

studied. Specifically, how the change of the weight level of support in the Armeo®Spring

device may affect the reaching performance of patients with severe stroke. This project

aimed to examine the role of the Armeo®Spring on reaching movements while using two

different gravity-support levels.

3

Significance of the Study

Stroke rehabilitation is an important public health issue that needs to be addressed

by all health care professionals. It gains this importance because of the increase of the

prevalence and incidence of those with stroke disability due to population aging and

improved survival after the initial injury (Volpe et al., 2009). Krebs, Volpe, Aisen, &

Hogan (2000) described three ways to maximize the productivity in the delivery of

rehabilitation without sacrificing the quality of care patients receive. These three methods

include: develop evidence-based therapy, re-allocate personnel and tasks, and increase

the productivity of each caregiver that can be achieved by providing therapists with

appropriate tools.

The increase of the prevalence and incidence of stroke along with the economical

pressure and lack of human resources stimulates the interest in the use of robot-assisted

techniques to enhance the efficiency and effectiveness of post-stroke rehabilitation

(Burgar et al., 2011). On the other hand, it is important to investigate the efficiency of

each device and to make sure that it provides realistic clinical expectations as it is

supposed to achieve.

Post-stroke rehabilitation has tremendous implication for most of health care

professions and stands as an intrinsic part of occupational therapy practice. As

“Occupational therapy (OT) aims at facilitating task performance by improving relevant

performing skills or developing and teaching compensatory strategies to overcome lost

performance skills” (Steultjens et al., 2003). Providing therapists with the proper tools to

4

promote the quality of care provided will play a key role in enhancing occupational

therapy interventions and enable therapists to increase their productivity levels.

Background and Literature Review

The World Health Organization (WHO) defines stroke as “a clinical syndrome

with rapidly developing clinical signs of focal or global disturbance of cerebral function,

lasting more than 24 hours or leading to death with no apparent cause other than of

vascular origin.” (Broeks, Lankhorst, Rumping, & Prevo, 1999). It occurs when a blood

clot blocks an artery, which is a blood vessel that carries blood from the heart to the

body, or when a blood vessel bursts, causing an interruption in the blood flow to an area

of the brain. When either of these scenarios happens, brain cells begin to die leading to

brain damage (National Stroke Association, 2011). In addition, stroke, or cerebrovascular

accident (CVA), can be defined as “a sudden ischemic or hemorrhagic disturbance in the

blood supply to brain tissue that results in partial loss of brain function.” (Prange,

Jannink, Groothuis-Oudshoorn, Hermens, & Ijzerman, 2006).

Types of Stroke

Stroke has been categorized by the National Stroke Association (2011) according

to its underlying cause into two major types: ischemic and hemorrhagic stroke.

Ischemic Stroke. Ischemic stroke accounts for about 87 percent of all cases

(American Heart Association, 2011). Naturally, blood clotting is a beneficial

physiological process which aims to slow and eventually stop the bleeding from a wound.

5

However, these clots maybe a source of danger in the case of stroke because they can

block arteries and cut off blood flow and oxygen supply to certain areas of the brain, A

process which is known as Ischemia (National Stroke Association, 2011).

Ischemic stroke has two subtypes according to the clot formation origin: (a)

embolic stroke, (b) thrombotic stroke.

a. Embolic Stroke: the blood clot that causes embolic stroke is formed somewhere in

the body, usually the heart, and travels through the bloodstream to the brain. The

clot travels in the brain blood vessels until it reaches a small enough vessel to

block its passage causing a stroke. The medical word used to describe this type of

blood clot is embolus (National Stroke Association, 2011).

b. Thrombotic Stroke: the blood clot causing this type of strokes is formed on a

blood vessel causing the blockage to one or more of the arteries supplying blood

to the brain. The process leading to this blockage is known as thrombosis

referring to the medical description for a clot that forms on a blood-vessel deposit

which is thrombus. This blood clot can happen as a result of unhealthy blood

vessels clogged with a buildup of fatty deposits and cholesterol. The body reacts

regarding these buildups as a multiple, tiny and repeated injuries to the blood

vessel wall, as if a bleeding from a wound is present, it responds by forming clots.

Two types of thrombosis can cause stroke: large vessel thrombosis and small

vessel disease/lacunar infarction (National Stroke Association, 2011).

i. Large Vessel Thrombosis: large vessel thrombosis is the most common and

best understood type of thrombotic stroke. Most of this type of strokes is

6

caused by a combination of long-term atherosclerosis followed by rapid

blood clot formation. Patients who have suffered this type of brain attack are

more likely to have coronary artery disease, and heart attack is a frequent

cause of death (National Stroke Association, 2011).

ii. Small Vessel Disease/Lacunar Infarction: occurs when blood flow is

blocked to a very small arterial vessel. Little is known about the causes of

small vessel disease, but it is closely linked to high blood pressure or as

known as hypertension (National Stroke Association, 2011).

Hemorrhagic Stroke. Hemorrhagic stroke accounts for about 13 percent of

stroke cases (American Heart Association, 2011). This type of strokes is caused by the

breakage or burst of a blood vessel in the brain. The medical word that describes this type

of breakage is hemorrhage which can be caused by a number of disorders that affect the

blood vessels, including long-standing high blood pressure and cerebral aneurysms. An

aneurysm is defined as a weak or thin spot on a blood vessel wall, which is usually

present at birth or develop over a number of years, and usually don't cause detectable

problems until they break (National Stroke Association, 2011).

Hemorrhagic stroke is categorized into two subtypes: (a) subarachnoid

hemorrhage and (b) intracerebral hemorrhage

a. Subarachnoid hemorrhage (SAH): when an aneurism bursts in a large artery on or

near the thin, delicate membrane surrounding the brain, the blood spills into the

area around the brain which is filled with a protective fluid, causing the brain to

be surrounded by blood-contaminated fluid (National Stroke Association, 2011).

7

b. Intracerbral hemorrhage: occurs when bleeding from vessels within the brain is

present. The primary cause of this type of hemorrhage is hypertension (National

Stroke Association, 2011).

Transient Ischemic Attack (TIA). Transient ischemic attack (TIA) is often

labeled as a “mini-stroke.” It is more accurately characterized as a “warning stroke”. Like

stroke, TIA is caused by a clot but the only difference between a stroke and TIA is that

with TIA the blockage of the blood vessel is transient (temporary). TIA symptoms occur

rapidly and last for a relatively short time (less than five minutes; the average is about a

minute). Unlike a stroke, when a TIA is over, there’s no permanent injury to the brain

(National Stroke Association, 2011).

Symptoms & Complications

According to the World Health Organization (WHO) (WHO, 2011) the most

common symptom of a stroke is sudden weakness or numbness of the face, arm or leg,

mostly on one side of the body. Other symptoms include: confusion, difficulty speaking

or understanding speech; difficulty seeing with one or both eyes; difficulty walking,

dizziness, loss of balance or coordination; severe headache with no apparent cause; as

well as fainting or unconsciousness.

The severity and effects of a stroke depend on where the stroke occurs in the brain

(location) and how much the brain is damaged (lesion size) (Volpe, Krebs, & Hogan,

2001; WHO, 2011), resulting in deficits of the cognitive, sensory, affective, and motor

functions (Krebs, Volpe, Aisen, & Hogan, 2000).

8

The most disabling motor deficit following stroke is the loss of arm function.

About 85% of stroke survivors have a sensorimotor deficit in the arm which is

characterized by muscle weakness, abnormal muscle tone, abnormal movement

synergies, lack of mobility between structures at the shoulder girdle, and incoordination

during voluntary movement (Cirstea, Ptito, & Levin, 2003). Deficits in the coordinated

use of the limb are most evident in the limb contralateral to the damaged side of the brain

(Levin, 1996). Attempts to make goal-directed movements with the affected limb in

stroke survivors are typically characterized by decreased range of motion (ROM),

movement speed, smoothness, coordination, and abnormal pattern of muscle activation

(Johnson, Feng, Johnson, & Winters, 2007; Levin, 1996).

The development of upper extremity spastic paresis is a common complication

following stroke. It is comprised of positive and negative symptoms that occur to varying

degrees in each patient. Positive symptoms include spasticity, hypertonia, increased

muscle stiffness, and excessive co-contraction between agonist and antagonist muscles

(Leonard, Gardipee, Koontz, Anderson, & Wilkins, 2006). Spasticity is defined as a

velocity dependent hyperexcitability of muscles to stretch and is characterized by

exaggerated tendon reflexes, increased resistance to passive movement and hypertonia

resulting from loss of upper motor neuron inhibitory control (Watkins et al., 2002).

Negative symptoms include muscle paresis and discoordination (Leonard, Gardipee,

Koontz, Anderson, & Wilkins, 2006). After stroke, spasticity contributes to motor

impairments and activity limitations and may become a severe problem for some patients

(Sommerfeld, Eek, Svensson, Holmqvist, & von Arbin, 2004). In the upper limb,

spasticity may present in two types of synergies. A flexor synergy which consists of

9

forearm supination and elbow flexion combined with shoulder flexion, abduction and

external rotation, or extensor synergy which is characterized by forearm pronation and

elbow extension associated with shoulder extension, adduction and internal rotation

(Levin, 1996).

Motor Recovery

Generally, the largest proportion of the recovery process takes place during the

weeks and months that immediately follow stroke occurrence (Volpe, Krebs, & Hogan,

2001). Even though, the rehabilitation process should not be stopped after the acute

rehabilitation hospital event. In fact, home training or home training enhanced with

devices managed by therapists has the potential to contribute to recovery goals (Volpe,

Krebs, & Hogan, 2001).

Motor learning have been defined loosely by motor control scientists by

considering it a fuzzy term that encompasses motor adaptation, skill acquisition, and

decision making (Huang & Krakauer, 2009). The neuro-rehabilitation science is built up

on two basic assumptions, the first one is that motor learning principles apply to motor

recovery, and the second assumption is that patients can learn (Huang & Krakauer, 2009).

The recent motor control models suggest that the central nervous system learns a

new novel task through practice by constructing a pattern of control variables or making

an internal model for that task, and once the new process is earned, it is stored in memory

and available for recall (Cirstea, Ptito, & Levin, 2003).

10

The majority of motor deficit recovery occurs within 6-months post-stroke

(Macclellan et al., 2005). According to the available scientific literature, post-stroke

rehabilitation intervention is suggested to be significantly more effective when it is

delivered in the early phase of recovery. Evidence supports that the better functional

outcome is determined by rehabilitation that is initiated promptly and based on intensive

multisensory stimulation which is associated with increased adaptive plasticity of the

brain in the early post-stroke stages (Masiero, Armani, & Rosati, 2011).

The restoration of motor function in the arm and leg after stroke has been

described as an ordered, predictable, stepwise progression by Twitechell (Twitchell,

1951). The initial stage of this progression is flaccid paralysis, after that the development

of a basic stereotypical synergy of voluntary movements, and then to normal patterns of

voluntary movements. Based on these observations Brunnstrom (Brunnstrom, 1966)

divided the progression into 6 sequential stages of motor recovery (Table 1) (Crow &

Harmeling-van der Wel, 2008). The early stages are characterized by the appearance of

spasticity and the development of stereotypic movement patterns while isolated joint

movements are jeopardized. In later stages, spasticity declines and the patient is able to

make movements out of synergy. Still later, isolated joint movement and control returns

(Levin, 1996).

To understand stroke recovery on a more mechanistic level, two main

assumptions are encompassing the recovery process. The first one is that parallel brain

regions in the unaffected hemisphere conduct the functions of the contra-lateral

hemisphere necrotic tissue by sending its commands via uncrossed pathways. The second

assumption is that the adjacent areas of undamaged brain tissue recognize and conduct

11

the functions of the necrotic tissue in the same hemisphere (Krebs, Hogan, Aisen, &

Volpe, 1998). The cortical maps reorganization process has been demonstrated in the

motor, sensory, auditory, and visual maps. Furthermore, the environment has an influence

on the degree of reorganization of the remaining undamaged cortex (as recent animal

studies on primates have shown) which suggest that exercising the patients’ affected

limbs might have a positive effect on neurological restoration of the limb function

(Krebs, Hogan, Aisen, & Volpe, 1998).

Table 1. Brunnstrom & Twitechell motor recovery stages (Crow & Harmeling-van der Wel, 2008)

Twitchell Brunnstrom

Flaccid paralysis with areflexia Stage 1

Flaccid paralysis

Reflex activity returns/spasticity develops Stage 2

Development of minimal movement in synergies

Voluntary movement in stereotyped flexor and

extensor synergies/spasticity is at maximal level

Stage 3

Voluntary movement synergy dependent

Voluntary movement with breaking up of

synergies/spasticity is reducing

Stage 4

Some movements out of synergy

Stage 5

Movements almost independent of synergy

Normal voluntary movement with normal speed and

dexterity/slight hyperactivity of the tendon reflexes

Stage 6

Normal movement with normal speed

Stroke survivors have the tendency to use their unaffected arm in real world tasks

(Johnson, Feng, Johnson, & Winters, 2007). Part of the standard treatment for the

sensorimotor impairment focus on teaching patients to use the unaffected limb to adapt,

compensate, and improve motor abilities with respect to feeding, grooming, and toileting

(Volpe et al., 2009). The other emphasis of acute rehabilitation is to teach compensatory

rather than restorative methods (Burgar et al., 2011). On the other hand, different studies

reported that several approaches, including repetitive passive exercises, forced use of the

paretic limb or constraint-induced movement therapy, functional electrical stimulation,

increased amounts of therapy including external manipulation, and biofeedback provided

12

positive outcomes on the motor recovery of the affected limb (Krebs, Volpe, Aisen, &

Hogan, 2000; Masiero, Armani, & Rosati, 2011).

The literature supports that in order for the therapy to be effective it should

contain elements of repetition, intense practice, motivation, and task application. Patient

involvement and empowerment along with the use of functional and purposeful tasks in

an enriched environment play a key role in increasing patient’s motivation and recovery

(Wisneski & Johnson, 2007).

The rehabilitation process of the impaired upper limb focuses on reducing

impairment and improving independent function on various activities of daily living

(ADLs) salient to patients’ real life environment. If the patients are able to transfer motor

and functional gains achieved during supervised therapy to their daily life this process is

considered effective and successful (Johnson, Feng, Johnson, & Winters, 2007).

Robot-assisted Therapy

The use of rehabilitation robots to complement standard post-stroke rehabilitation

is a new promising tradition that has been developed intensively in the past few decades

(Masiero, Armani, & Rosati, 2011). Examples of upper extremity rehabilitation robots

that are currently available in the market or in research labs are Armeo®Spring (Hocoma,

Inc), Armeo®Power (Hocoma, Inc), ARMin (Nef, Mihelj, & Riener, 2007), MIT-

MANUS (Krebs, Hogan, Aisen, & Volpe, 1998), and T-WREX (Housman, Scott, &

Reinkensmeyer, 2009). They have been developed to aid in rehabilitation, alter the

physical burden on a therapist to overcome the limited availability of one-to-one stroke

rehabilitation, and potentially improve a clinic’s productivity (Wagner et al., 2011;

13

Wang, Wang, Zhang, Wang, & Wang, 2011). Robotic devices can provide repetitive,

task-specific, and high-intensity interactive treatment of the impaired limb. They can also

measure patients’ motor progress objectively and measure changes in movement

kinematics and forces (Masiero, Armani, & Rosati, 2011).

A common misperception about robot-assisted therapy is that it would ultimately

replace human-administered therapy (Krebs, Volpe, Aisen, & Hogan, 2000). In fact, it is

most appropriate to consider the robot as an advanced tool that is used under the therapist

supervision to implement relatively simple and labor-intensive therapies (Masiero,

Armani, & Rosati, 2011). As the systematic reviews of robot-assisted therapy suggest,

these devices met the criteria for improving proximal upper extremity strength and have

the potential to promote motor recovery to a greater extent than traditional therapy

(Housman, Scott, & Reinkensmeyer, 2009). Individuals who suffer from acute or chronic

stroke and receive more therapy with a robotic device can recover more movement

ability, and those with chronic stroke who receive matched amounts of robotic and

conventional therapy produced comparable therapeutic benefits (Sanchez et al., 2006).

Rehabilitation robots for the upper limb can be classified into passive, active, and

interactive systems (Loureiro, Harwin, Nagai, & Johnson, 2011; Riener, Nef, &

Colombo, 2005). In passive systems, no actuation is implemented to move patient limbs.

Instead, the system constrains the patient’s arm to a determined range of motion. They

often consist of mechanical linkages that move easily when pushed and their technical

components typically include stiff frames, bearings and pulleys, and ropes with counter-

weights (Loureiro, Harwin, Nagai, & Johnson, 2011; Riener, Nef, & Colombo, 2005).

Active systems are equipped with electromechanical, pneumatic, hydraulic and other

14

drives to move the patient’s arm actively through a predefined path. Either the devices

are open-loop controlled, or simple position-control strategies are implemented to take a

patient’s arm from a predefined position to a new position using a certain velocity profile

(Loureiro, Harwin, Nagai, & Johnson, 2011; Riener, Nef, & Colombo, 2005). Interactive

systems react to the patient’s input and characterized not only by actuators but also by

sophisticated impedance and other control strategies. They are usually back-drivable and

possess low, intrinsic, end point impedance (Loureiro, Harwin, Nagai, & Johnson, 2011;

Riener, Nef, & Colombo, 2005).

Gravity Compensation

Little information regarding the effects of gravity compensation on upper limb

recovery after stroke was found in the literature. It was reported that stroke patients

showed an improved arm function after nine weeks of training using gravity

compensation provided by sling suspension, which suggest that the application of gravity

compensation may be considered a valuable tool to stimulate functional improvement in

stroke rehabilitation (Prange et al., 2009). Another research has shown that gravity

compensation in upper limbs decreases the required shoulder abduction torques during

two dimensional reaching movements at shoulder height, causing a decrease in coupled

elbow flexion leading to an increase in the range of elbow extension (Krabben et al.,

2012). Furthermore, the maximal reaching distance during a 3-dimensional movement,

starting with the hand at waist height and reaching to a target at shoulder height, is

slightly larger when gravity compensation is applied to the arm of stroke patients

(Prange et al., 2009).

15

Reaching Studies

Many studies have examined the reaching movements in stroke (Archambault,

Pigeon, Feldman, & Levin, 1999; Cirstea, Ptito, & Levin, 2003; Jannink et al., 2007;

Kamper, McKenna-Cole, Kahn, & Reinkensmeyer, 2002; Krabben et al., 2012; Leonard,

Gardipee, Koontz, Anderson, & Wilkins, 2006; Levin, 1996; Prange et al., 2009). The

analytical variables that have been used to quantify the reaching movements varied

among different studies, which included (but not limit to) speed accuracy and efficiency

of reaching, peak wrist velocity, endpoint error, reach path ratio, peak speed ratio,

number of speed peaks, interjoint coordination, linearity of hand motion, movement

direction variability, muscle co-contraction, muscle activation, and trunk compensation.

Different analysis methods have been used to determine the movement onsets and offsets.

For example, Cirstea and Levin (2000) used the times at which the tangential velocity

exceeded or fell below 10% of the peak velocity, while Butler et al. (2010) defined the

beginning (i.e., initiation) of each cycle as the first instant when the velocity of the wrist

marker exceeded 5% of peak reaching velocity. Kinematic data were low-pass filtered at

5 Hz and 6 Hz (Kamper, McKenna-Cole, Kahn, & Reinkensmeyer, 2002; Wagner,

Dromerick, Sahrmann, & Lang, 2007). In general, studies have shown that in stroke

subjects multi-joint pointing movements are characterized by decreased movement speed

and increased movement variability, by increased movement segmentation and by spatial

and temporal incoordination between adjacent arm joints with respect to healthy subjects

(Archambault, Pigeon, Feldman, & Levin, 1999; Cirstea & Levin, 2000). Stroke subjects

also showed the use of compensatory movement patterns (Cirstea & Levin, 2000).

Previous reaching studies available in the literature are illustrated in Appendix.

16

Specific Aims and Hypothesis

Aim 1: To compare reaching biomechanics between two different gravity-support levels

(mild and moderate weight support) in a healthy control using the gravity-supporting

exoskeleton apparatus (Armeo®Spring)

Hypothesis 1: We hypothesize that different gravity-support levels do not affect

reaching movements in healthy controls.

Aim 2: To compare reaching biomechanics between two different gravity-support levels

(mild and moderate weight support) in a stroke subject using the gravity-supporting

exoskeleton apparatus (Armeo®Spring)

Hypothesis 2: We hypothesize that the stroke subject will improve the reaching

performance under the higher weight support condition. Specifically, we

hypothesize that the gravity-supporting facilitates the stroke subject’s upper limb

movement and thus the stroke subject is able to complete the task more efficiently

and with less physical efforts. The moving time would reduce and mean reaching

speed would increase.

Aim 3: To compare the biomechanics of reaching movements between a healthy control

and a stroke subject using the gravity-supporting exoskeleton apparatus (Armeo®Spring)

Hypothesis 3: We hypothesize that, comparing to the healthy control, the stroke

subject would have (a) a longer moving time, (b) slower moving speed, and (c)

different muscle activation patterns in the muscles acting against gravity in the

upper limb during reaching.

17

Methods

Participants

This study was conducted at the Gait and Biodynamics Laboratory at the

University Services and Research (USR) building on the University of Wisconsin

Milwaukee campus. The recruitment process was done through flyers distributed around

campus and in the surrounding community and through word-of-mouth. Subjects

completed a questionnaire over the phone to determine their eligibility. The study took

approximately 2 hours over a one-day course for each participant. Prior to testing, the

participants signed an informed consent form to participate in the study per the protocol

approved by the University of Wisconsin Milwaukee Institutional Review Board for

human subject research.

For the proposed study, one stroke subject and one healthy control were recruited

in this study after gaining their informed consent.

Inclusion and exclusion criteria

Qualified participants must be between the age of 18 to 80 for both control and

experimental groups. Individuals who have musculoskeletal disorders, sensory disorders,

and/or a history of osteoarthritis were excluded from the study. Individuals who do not

speak English were not recruited in the study. Individuals who weight over 300 pounds

were not included due to the size of the Armeo®Spring device. Women who were

pregnant or expecting to be pregnant were not recruited for this study to protect the

18

unborn child and the mother from the risks during testing. Stroke survivors were

excluded from the study if they had more than 3 score in the modified Ashworth Scale,

onset of stroke is less than 6 months, and/or unstable health conditions in the judgment of

the Principal Investigator (PI) and Co-PI would prevent them from participating in this

study.

Device: Armeo®Spring

The Armeo®Spring (Figure 1) is a gravity-supporting exoskeleton apparatus that

contains no robotic actuators. It is the commercialized product of Therapy Wilmington

Robotic Exoskeleton (T-WREX) (Housman, Scott, & Reinkensmeyer, 2009) which has

been re-designed by Hocoma, Inc. with user-friendly software and hardware interface to

be used in the routine clinical settings. The main structure of the device consists of an

arm exoskeleton with integrated springs providing a 5 degree-of-freedom movement at

the shoulder, elbow, and wrist levels. It embraces the whole arm, from shoulder to hand,

and counterbalances the weight of the patient’s arm providing a sense of arm flotation at

all positions in the available workspace. The device level of weight support at the arm

and forearm level can be adjusted to provide variable levels of weight support. The length

of both the arm and forearm can be adjusted to fit the configuration of the limb and to be

used by a wider variety of users. The device has a pressure sensitive handgrip which

works as an input device for exercises and can be used as a computer interface for the

software and computer games. The handgrip can also be removed for functional training

of real life tasks. The device contains built-in sensors which enables it to be used as a 3D

input device for computer game playing with the affected arm.

19

The device comes with computer software (Armeocontrol) which contains an

extensive library of game-like movement exercises. The games are designed to mimic

functional arm movements, to provide training in a simple virtual reality environment,

and to achieve the goal of enabling repetitive task-specific practice.

In all functional exercises, the exercises are mapped into a cubic workspace,

which can be adjusted to the movement abilities of each individual. Before starting the

exercise session, the workspace has to be defined (i.e., the maximum distance a person

can bring his/her hand up, down, left, and right, and how far and close to the body while

using the Armeo®Spring) to adjust to the movement abilities of each individual.

Figure 1. The Armeo®Spring study setup

Computer

software

interface

Markers for

motion caption

system

20

Armeo®Spring Weight-Support System

The level of weight support is device related (no standardized measuring units

have been used to describe level of support) for both arm and forearm (Figure 2). Using

the device scale of arm (A-K) and forearm support (1-5), the mild weight-support level

was defined as (C – D) support levels and (1 – 2) support levels at the arm and forearm

respectively. The moderate weight-support level was defined as (E – G) support levels

and (3 – 4) support levels at the arm and forearm respectively. In order to clarify the

weight-support system of the device, the differences between variable weight-support

levels in both arm and forearm were measured manually using a tension gauge. Results

are displayed in table 2.

Figure 2. Armeo®Spring weight support system

There is a load cell embedded just underneath

the middle of the forearm brace to record the

tension force (i.e., vertical supporting force).

21

Table 2: Armeo®Spring support levels

* The moments at the shoulder level were computed for shoulder flexion movement only. The moments at

the elbow joint level were computed for elbow flexion movements.

Game: Fruit Shopping

The Fruit Shopping (Figure 3) is one of the games included with Armeocontrol

games library. It is about picking apples and placing them in a shopping cart. The apples

are green in color and will show up one at a time across a computer screen while the

shopping cart is placed at the lower left corner of the screen (for a right-hand user). To

complete the game, the user should move a hand-like pointer using the Armeo®Spring

from the initial start position to reach an apple that turns from green to red in color.

When the pointer is over the red apple, the user should squeeze/grasp the pressure

sensitive handgrip of the Armeo®Spring device to hold the apple and transport the apple

to the shopping cart. When the color of the cart changes the user should take the pressure

off the device handgrip to release the apple. The phases and tasks of the Fruit Shopping

cycle are displayed in Figure 3.

Body Part ArmeoSpring Support

Measured Support

(N.m)

Arm

A 0

B 0.79

C 1.65

D 2.43

E 3.24

F 4.01

G 4.92

H 5.91

I 6.91

J 7.85

K 8.73

Forearm

1 0

2 0.81

3 1.63

4 2.41

5 3.28

22

Figure 3. The print screen of the Fruit Shopping game

Data Collection

Three data collection instruments were used for this study to examine the changes

that may occur when using the two levels of weight support of the Armeo®

Spring device.

First, Motion Analysis tracking system (Motion Analysis Corporation, Santa Rosa, CA)

was used to record markers (placed on the subject) positions at 100 Hz using 10 infrared,

3-dimentional cameras. Second, muscle activity patterns were measured using surface

electromyography (EMG) electrodes using the Trigno ™ 16-channels wireless EMG

system (Delsys Inc., Boston, MA). EMG signals were amplified (x1000) and recorded at

1000 Hz sampling rate. The third instrument was a low profile tension and compression

load cell (Futek Advanced Sensor Technology Inc., Thomas Irvine, CA) which had been

added at the forearm level of the Armeo®Spring device. The load cell collected the

vertical forces generated by the limb at 1000 Hz sampling rate.

4. Transporting

5. Releasing

2. Reaching

1. Initiation

3. Grasping

23

Procedure

Before data collection, subjects were informed to wear tight fitting clothing on the

scheduled data collection date. Upon their arrival, and after signing the informed consent

form, clinical assessments including the Fugl Meyer-Upper Arm Scale and the modified

Ashworth Scale were administered by the PI to assess the stroke severity of the stroke

subject. Afterwards, a total number of 26 reflective markers were placed on the subjects’

chests, backs, shoulders, upper arms, and forearms using a double-sided adhesion tape

directly to the skin. Marker names and positions are illustrated in Table 3. After that, a

total of 7 bipolar surface EMG electrodes were placed to record the activities in the

anterior deltoid, middle deltoid, biceps, triceps, extensor digitorum, flexor digitorum, and

brachioradialis muscles. Before applying the electrodes, the skin beneath the electrode

placing positions was cleaned with alcohol prep pad. Excessive hair, if present, was

shaved using a razor. After applying the electrodes, an initial signal check was performed

to ensure that the EMG electrodes were functioning. Then, the Maximal Voluntary

Contraction (MVC) of each muscle was recorded.

After applying all the markers and EMG electrodes, subjects wore the

Armeo®Spring device while sitting on a stationary chair with no arm support. Then, the

subjects were instructed to practice the Fruit Shopping game by using Armeo®

Spring as

an input device for 3-5 minutes. After that, three trials were recorded for each subject

while using the Armeo®Spring with mild weight support and three trials with moderate

weight support. Within each trial of the Fruit Shopping game, the computer continued to

provide the subject an apple for reaching until (a) the end of time (total duration is 3

minutes), or (b) the subject had picked up all the apple (n =17) within the time limit.

24

Subjects were instructed to rest his/her hand after releasing the apple in the shopping cart

for three seconds before reaching another apple at the cart location.

Table 3. Markers used in the motion caption procedure

Abbreviation Marker name Abbreviation Marker name

INJU Incisura Jugularis LELB Lateral epicondyle (Elbow)

STCL Sternoclaviculare MELB Medial epicondyle (Elbow)

XIPH Processus Xiphoideus LWRI Radial styloid (Lateral wrist)

ACRO Acromioclaviculare MWRI Ulnar styloid (Medial wrist)

C7 7th

cervical vertebrae 1PHA Tip of 1st phalanx

T4 4th

thoracic vertebrae 2PHA Tip of 2nd

phalanx

TRSC Trigonum Scapulae 2MCP 2nd

Metacarpophalgel

INSC Angulus Inferior LHAN Lower hand

MDSC dynamic CHAN 5th

MCP

AASC Angulus Acromialis S-RU Superior forearm

S-HU Superior humerus I-RU Lower forearm

I-HU Lower humerus FORM Forearm triangle – medial

FORC Forearm triangle – central FORL Forearm triangle – lateral

MVC Procedure

Subjects were asked to contract each muscle at the highest level they could

sustain for ∼3 s in duration by pushing up against a research assistant’s pushing force.

The procedure was repeated three times with a pausing period of 10 s. The greatest value

of the trials was recorded as the MVC level. This process was repeated for the seven

muscles and each muscle was tested according to the manual muscle testing

recommended position. Testing positions are illustrated in table 4.

Kinematic Model

Joint angles were calculated according to the kinematic model proposed by the

ISB recommendations on definitions of joint coordinate systems (Wu et al., 2005). The

25

model defined a set of segment coordinate systems and used Euler angles to determine

the 3D joint angles. In order to find the glenohumeral joint flexion, abduction/adduction

and elbow flexion angles, three segment coordinate systems were defined for the

following segments: (1) thorax, (2) right upper arm, and (3) right forearm. The equations

used to define the three coordinate systems are illustrated in table 5.

The glenohumeral joint rotation center (GHJC) was estimated by taking 7cm of

the vertical offset (Y-direction) of the Acromioclaviculare marker (Schmidt, Disselhorst-

Klug, Silny, & Rau, 1999). The elbow joint center was defined as the midpoint between

lateral and medial epicondyle (MID_ELB) (Wu et al., 2005).

Due to the nature of the Armeo®Spring device and the experiment setting, some

of the markers were obstructed during the trials. In order to overcome this problem two

measured coordinate systems were developed to compensate the anatomical coordinate

systems of the upper arm and forearm. Two markers were added to the upper arm (S_HU

and I_HU) and two markers to the forearm (FORC and FORL) to create the two

measured coordinate systems. Also, the marker of the 8th

thoracic vertebrae was replaced

with a marker on the 4th

thoracic vertebrae (T4) as the first marker was obstructed by the

backrest of the chair that was used during the experiment.

A static trial was recorded for each subject with all markers (anatomical and

measured markers) in order to define two transformation matrices between anatomical

and measured coordinate system of the upper arm and forearm. During the experiment

trials (dynamic trials) the problematic markers were removed and the measured

coordinate systems of the upper arm and forearm were recorded and converted back to

26

the anatomical coordinate systems using the two transformation matrices defined in the

static trial.

The angles between coordinate systems were calculated using Euler rotation

following ZX’Y’’ sequence. The Z-axis is the flexion/extension axis of the glenohumeral

and elbow joints. The X-axis is the abduction/adduction axis of the glenohumeral and

elbow joints, and the Y-axis internal/external axis of the upper arm and forearm.

Table 4. MVC testing positions

Muscle Position

Anterior deltoid While seated and elbow in slight flexion position, the subject flex

their arm to 90° against the resistance force provided above the

elbow joint

Middle deltoid While seated and elbow in slight flexion position, the subject

abduct their arm to 90° against the resistance force provided above

the elbow joint

Biceps While seated and with slight shoulder flexion and forearm is

supinated, the subject flex elbow to 90 against the resistance force

provided above the wrist joint

Triceps While seated and with slight shoulder flexion and forearm is

supinated, the subject extend elbow from 90 of flexion against the

resistance force provided above the wrist joint

Extensor digitorum While forearm resting on a table and pronated, the subject extend

their wrist against the resistance force provided at subject’s hand

Flexor digitorum While forearm resting on a table and supinated, the subject flex

their wrist against the resistance force provided at subject’s hand

Brachioradialis While seated and with slight shoulder flexion and forearm is

pronated, the subject flex elbow to 90 against the resistance force

provided above the wrist joint

Table 5. Anatomical coordinate systems

Segment Coordinate System

Thorax Origin: GHJC

Yt: ((INJU+C7)/2) – ((XIPH+T4)/2), pointing upward

Zt: cross product of Yt and (C7-INJU), pointing to the right

Xt: cross product of Yt and Zt, pointing forward

Upper arm Origin: GHJC

Yh: GHJC – MID_ELB, pointing to GHJC

Zh: cross product of (MWRI - MID_ELB) and Yh, pointing to the right

27

Xh: cross product of Yh and Zh, pointing forward

Forearm Origin: MWRI

Yf: MID_ELB – MWRI, pointing proximally

Xf: cross product of Yf and (LWRI – MWRI), pointing forward

Zf: cross product of Xf and Yf, pointing to the right

Data Analysis

The data collected using motion capture system, the load cell, and Trigno ™

wireless EMG system were processed and labeled using Cortex 2.4.0 motion analysis

software. The motion analysis data were low-pass filtered at 12 Hz using a Butterworth

filter (Butler et al., 2010). Joint angles for three primary motions of the arm:

glenohumeral joint flexion-extension, abduction-adduction and elbow flexion–extension

were calculated using Matlab (MathWorks, Natick, MA). Each EMG sensor is equipped

with band-pass filter with cut-off frequencies 20- 450 Hz. The EMG signal was full-wave

rectified and smoothed using Root Mean Square (RMS) function using 0.3 seconds time

window (Stoeckmann, Sullivan, & Scheidt, 2009). The muscle activations were measured

as percentages of the MVC value.

The Fruit Shopping cycle consisted of five phases: (1) initiation, (2) reaching, (3)

grasping, (4) transporting, and (5) releasing & resting. The cycle phases were defined

based on the 2MCP marker (base of the index finger on the dorsal side of the hand)

coordinates and velocity. The resting periods between the cycles were used to initially

segregate the cycles. The beginning (i.e., initiation) of each cycle was identified as the

first instant when the velocity of the 2MCP marker exceeded 5% of peak reaching

velocity and continued to increase until it reached 30% of peak reaching velocity while

the 2MCP marker coordinates increased in two axes at least (Butler et al., 2010). The

reaching phase started when the peak reaching velocity exceeded 30% and continued

28

until 2MCP marker reach back to 5% of its peak reaching velocity. The grasping phase

started when the 2MCP marker reached 5% of its peak velocity after the reaching phase

and ended when the 2MCP marker reach back to the last 5% of its peak velocity before

reaching to 30% again. The transporting phase started when the velocity of 2MCP marker

exceeded 5% of its peak velocity following grasping phase and ended when the 2MCP

marker reached back to 5% of its peak velocity. Then, the end of the cycle was signified

by a decrease in 2MCP marker velocity to less than 5% of the peak velocity upon

returning the arm to the initial position.

For each phase, three kinematic parameters (completion time, moving velocity,

acceleration) and one kinetic parameter (arm vertical supporting force) were calculated.

Velocity and acceleration parameters were computed based on the 2MCP marker using

the 3-point central difference method. In addition, the average magnitude of the EMG

envelope was calculated for each phase. For visual inspection purpose, we plotted the

joint angles during one reaching cycle and compared the changes under mild and

moderate weight support conditions.

Independent t-test was used to compare between-group differences (stroke

subjects vs. healthy controls). Sample t-test was used to compare within group

differences (i.e., data from the same stroke subject or data from the same healthy control).

Results

Two subjects were recruited for this case study. A healthy control subject (female,

35 years, 110 lb, 1.52m, right side dominant) and a stroke subject (female, 54 years, 110

29

lb, 1.47m, right side dominant). The stroke subject had a stroke for 18 months in her right

side with a Fugl-meyer score of 27/66. Descriptions of subjects’ mild and moderate

weight-support levels provided by the Armeo®Spring device are illustrated in table 6.

Table 6. Armeo®Spring mild and moderate weight-support levels for stroke and healthy subjects.

Subject Body Part Level of Support Support Difference

(N.m) Mild Moderate

Healthy Arm D G 1.58

Forearm 2 4 1.63

Stroke Arm C E 2.50

Forearm 1 3 1.61

* Different baseline support (i.e., mild weight support) was adjusted accordingly depending on the weight

of the subject’s arm, such that with the mild weight support provided by the Armeo®Spring the subject’s

hand was floating just above the knee height in a sitting position. With the moderate weight support, which

was increased with 2 to 3 units weight support (e.g., from C to E was a 2-level increase), the subject’s arm

was floating near the theoretic but not exceeding the shoulder height.

Kinematic parameters

The first two research hypotheses pertained to the within group differences in

reaching performance. As hypothesized within the healthy control subject, results

demonstrated no significant differences in mean completion time, moving velocity, or

acceleration between mild to moderate gravity-support levels during all phases of the

cycle (Table 7). As predicted within the stroke subject (Table 8), results revealed a

significant decrease in the cycle mean completion time (p= 0.042). Specifically, a

significant decrease was found in mean completion time of the grasping phase (p=0.043)

between the two gravity-support levels (Figure 4). When comparing the moving velocity

within the stroke subject, a significant increase was found in the initiation phase moving

velocity (p=0.039) and a significant decrease was found in the grasping phase (p=0.048)

30

between two gravity-support levels. No significant differences were found in all phases

of the cycle when comparing the movement acceleration between the two gravity-support

levels.

Figure 4. Mean completion time between the two gravity-support levels

Table 7. Kinematic parameters of the healthy subject with mild & moderate weight support

Support

Level Parameter

Phase

Initiation Reaching Grasping Transporting Cycle

Mild

Completion

Time

Mean

(s) 0.21 1.05 0.89 1.30 3.46

SD 0.06 0.28 0.39 0.60 0.74

Velocity

Mean

(mm/s) 85.97 220.24 30.38 198.64 153.76

SD 5.13 29.02 7.89 20.72 26.73

Acceleration

Mean

(mm/s2)

577.10 -127.63 4.88 -2.18 -1.51

SD 64.36 18.40 3.66 1.96 2.75

Moderate

Completion

Time

Mean

(s) 0.23 1.11 0.83 1.21 3.38

SD 0.06 0.29 0.35 0.15 0.39

Velocity

Mean

(mm/s) 87.30 222.03 29.96 195.88 145.95

SD 8.30 18.14 5.85 22.19 26.14

Acceleration

Mean

(mm/s2)

556.46 -120.61 3.70 -2.69 -1.50

SD 58.83 23.89 3.89 2.71 2.82

0

2

4

6

8

10

12

Initiation Reaching Grasping Transporting Duration

Me

an C

om

leti

on

Tim

e (

s)

Phase

Mild Support (Healthy)

Moderate Support (Healthy)

Mild Support (Stroke)

Moderate Support (Stroke)

*

*

31

Table 8. Kinematic parameters of the stroke subject with mild & moderate weight support

Support

Level Parameter

Phase

Initiation Reaching Grasping Transporting Cycle

Mild

Completion

Time

Mean

(s) 0.25 1.50 5.41 1.81 8.96

SD 0.16 0.95 5.60 0.49 6.06

Velocity

Mean

(mm/s) 50.47 124.80 54.50 90.49 77.10

SD 5.84 28.97 14.71 26.84 17.36

Acceleration

Mean

(mm/s2)

441.88 -68.59 -0.15 0.52 0.38

SD 263.92 41.05 3.72 3.19 0.84

Moderate

Completion

Time

Mean

(s) 0.22 1.34 2.98 1.75 6.28

SD 0.10 0.59 1.86 0.67 2.35

Velocity

Mean

(mm/s) 54.46 130.33 46.09 96.28 79.94

SD 8.33 34.13 16.71 28.62 18.28

Acceleration

Mean

(mm/s2)

442.27 -73.03 -1.78 1.90 0.20

SD 191.04 37.50 7.03 7.69 1.04

The final hypothesis pertained to between groups reaching performance

differences. As hypothesized, differences between the healthy control and the stroke

subject revealed a significant increase in the cycle mean completion time (p<.001) while

using mild gravity-support level. While using moderate gravity-support level, a

significant increase (p<.001) in the mean completion time was found between subjects,

specifically, significant increases in the mean completion time were found in all phases of

the cycle except the initiation phase. Also, significant decreases in the moving velocities

were found in all phases of the cycle between the healthy control and the stroke subject

under both conditions.

32

Joint Angles

After increasing the weight-support provided by the Armeo®Spring device, the

healthy control subject showed an increase in abduction and flexion degrees at the

glenohumeral joint level, and an increase in flexion degrees of the elbow joint. On the

other hand, the stroke subject showed a decrease in abduction degrees and an increase in

flexion degrees at the glenohumeral joint level, and a decrease in flexion degrees of the

elbow joint after increasing the weight-support level. Figure 5 displays the average joint

angles during the reaching cycle for the healthy subject (upper panel) and the stroke

subject (lower panel).

Forearm Vertical Forces

Results demonstrated an increase in the mean of vertical forces when changing

gravity-support levels from mild to moderate during all phases of the cycle in both stroke

and healthy subjects. Differences between the healthy control and the stroke subject

revealed an increase in the cycle mean of vertical forces (1.78 lbs) while using mild

gravity-support level. While using moderate gravity-support level, an increase in the

cycle mean of vertical forces (2.67 lbs) was found between subjects. The average vertical

forces for the two subjects during each phase of the reaching cycle are illustrated in table

9 for both weight-support levels.

33

Figure 5. Joint angle changes during the reaching cycle for the healthy subject (upper panel) and the stroke

subject (lower panel).

* 0 degree in shoulder abduction/adduction means that the upper arm is located at the side of the body with

no abduction or adduction, the positive increase in the angles indicate shoulder adduction and the negative

increase indicates shoulder abduction . 0 degree in shoulder flexion means that the arm is located at the side

of the body with no anterior flexion. 0 degree in elbow flexion means that the forearm is fully extended.

Electromyography (EMG)

Within the healthy control subject, EMG muscle activation patterns were the same

for all the muscle between mild and moderate gravity-support level. The average EMG

magnitude for anterior deltoid, middle deltoid, biceps, and brachioradialis were

significantly decreased during all the phases of the reaching cycle (p<.001 for all

34

muscles) when changing the weight-support level from mild to moderate support.

Furthermore, no significant difference was found in the average EMG magnitude for the

triceps, extensor digitorum, and flexor digitorum muscles during all phases of the

reaching cycle between the two weight-support levels. Table 10 displays the average

EMG magnitude between two support levels for the healthy control subject.

Within the stroke subject, the average EMG magnitude for the anterior deltoid,

biceps, and brachioradialis muscles were significantly decreased in all phases of the

reaching cycle when changing weight-support level from mild to moderate support. On

the other hand, the average EMG magnitude of the triceps muscle was significantly

increased in all phases of the cycle (p<0.001 during initiation, p=0.001 during reaching,

p=0.005 during grasping, and p<0.001 during transporting). No significant difference was

found in the middle deltoid muscle average EMG magnitude during the phases of the

cycle except a significant decrease in the reaching phase (p=0.006) between two weight-

support levels. Furthermore, no significant difference was found in the average EMG

magnitude for the extensor digitorum and flexor digitorum muscles during all phases of

the reaching cycle between the two weight-support levels. Table 11 displays the average

EMG magnitude between two support levels for the stroke subject.

When comparing two subjects under the two weight-support conditions, the

results revealed significant decrease in the average EMG magnitude for all muscles

during all phases of the reaching cycle except for the anterior deltoid muscle. Under the

mild weight-support condition, no significant difference was found in the average EMG

magnitude during the initiation, grasping, and transporting phases. Under the moderate

35

weight-support, no significant difference was found during the grasping and transporting

phases. P-values for between-subjects average EMG magnitude are illustrated in table 16.

Table 9. Vertical support forces for healthy control and stroke subject with mild & moderate weight

support

Subject Support

Level Vertical Force

Phase

Initiation Reaching Grasping Transporting

Healthy

Mild Mean (lb) 4.73 2.18 1.43 5.10

SD 0.32 0.58 0.43 0.57

Moderate Mean (lb) 7.39 5.03 4.72 7.38

SD 0.42 0.51 0.64 0.43

Stroke

Mild Mean (lb) 7.20 5.99 6.14 6.89

SD 0.43 0.15 0.68 0.85

Moderate Mean (lb) 8.44 7.78 7.59 8.25

SD 0.63 0.46 0.36 0.33

Table 10. Healthy subject EMG average magnitude (% of MVC)

Level of support

Phase EMG Ant.

Deltoid Mid.

Deltoid Biceps Triceps

Ext. Digitorum

Flex. Digitorum

Brachioradials

Mild Support

Initiation Mean 15.40 7.82 2.75 2.27 3.86 0.92 1.97

SD 1.96 1.20 0.72 0.14 0.65 0.18 0.39

Reaching Mean 21.42 11.73 5.31 2.35 4.68 1.02 2.34

SD 1.61 1.23 1.20 0.15 0.77 0.18 0.45

Grasping Mean 22.08 11.67 6.59 2.52 13.22 3.49 9.62

SD 3.73 1.83 1.31 0.21 2.60 0.78 2.71

Transporting Mean 14.76 6.58 4.52 2.52 11.56 3.52 9.07

SD 1.61 0.94 1.26 0.32 2.46 0.74 1.90

Moderate Support

Initiation Mean 9.17 3.97 0.65 2.36 3.64 3.31 1.28

SD 1.90 0.69 0.29 0.19 0.66 0.15 0.34

Reaching Mean 13.79 7.48 1.27 2.37 4.55 1.04 1.40

SD 1.25 1.11 0.26 0.16 0.56 1.37 0.38

Grasping Mean 12.21 6.47 1.51 2.63 12.15 3.26 7.28

SD 2.07 1.24 0.39 0.29 2.28 0.58 1.55


SD 1.61 0.92 0.11 0.13 1.46 1.06 0.82

36

Table 11. Stroke subject EMG average magnitude (% of MVC)

Level of

support Phase EMG

Ant.

Deltoid

Mid.


Ext.

Digitorum

Flex.

Digitorum Brachioradials

Mild

Support

Initiation Mean 15.66 12.33 68.77 15.03 18.07 16.71 26.27

SD 5.79 2.79 23.06 3.60 6.35 3.21 7.74

Reaching Mean 33.27 18.89 79.06 15.82 28.31 21.71 45.78

SD 14.67 5.90 18.28 3.82 7.13 4.25 9.71

Grasping Mean 30.12 21.76 87.02 24.69 43.50 39.50 76.13

SD 16.08 10.33 8.94 12.21 5.16 7.92 6.53


SD 5.56 4.25 10.91 6.86 5.19 7.78 6.66

Moderate

Support

Initiation Mean 6.41 10.38 22.28 35.97 19.86 18.04 16.25

SD 1.51 5.19 11.40 7.49 18.40 11.22 8.69

Reaching Mean 8.03 11.64 30.96 31.53 21.70 19.91 23.81

SD 2.08 2.47 9.53 8.68 14.26 9.45 10.12

Grasping Mean 10.14 14.48 44.25 40.52 40.08 34.10 49.89

SD 3.00 0.96 13.26 6.78 9.42 7.27 9.70


SD 1.60 1.98 12.88 9.25 4.95 4.47 2.94

Table 12. Healthy subject EMG minimum magnitude (% of MVC)

Level of

support Phase EMG

Ant.

Deltoid

Mid.


Ext.

Digitorum

Flex.


Mild

Support

Initiation Mean 13.30 6.24 1.90 2.18 2.91 0.78 1.54

SD 2.51 1.57 0.86 0.12 0.80 0.19 0.37

Reaching Mean 16.62 8.96 3.47 2.12 3.58 0.80 1.81

SD 1.77 1.00 0.73 0.06 0.89 0.15 0.39

Grasping Mean 17.75 8.89 5.13 2.23 5.96 1.26 2.95

SD 2.87 1.09 1.06 0.15 1.06 0.26 0.73


SD 1.57 0.74 1.40 0.11 2.96 0.86 2.64

Moderate

Support

Initiation Mean 7.10 3.04 0.50 2.48 1.68 0.60 1.15

SD 2.02 0.69 0.27 0.18 0.61 0.07 0.30

Reaching Mean 10.61 4.67 0.74 2.20 1.39 0.56 0.97

SD 1.73 0.87 0.23 0.08 0.77 0.08 0.33

Grasping Mean 9.28 5.10 1.18 2.28 3.66 1.02 2.28

SD 2.20 1.19 0.36 0.22 1.49 0.41 1.13


SD 0.76 1.05 0.01 0.23 1.50 0.38 1.27

37

Table 13. Stroke subject EMG minimum magnitude (% of MVC)

Level of

support Phase EMG

Ant.

Deltoid

Mid.


Ext.

Digitorum

Flex.


Mild

Support

Initiation Mean 11.42 8.88 62.62 13.61 15.92 14.71 23.91

SD 6.60 3.71 22.30 2.77 5.89 3.35 7.24

Reaching Mean 16.26 10.84 54.71 12.15 17.31 14.73 23.93

SD 9.44 4.56 22.61 0.99 6.21 3.25 7.99

Grasping Mean 15.67 9.67 58.85 12.85 30.23 22.51 45.41

SD 13.08 6.09 10.59 1.39 7.91 4.77 10.88


SD 0.27 0.93 12.86 3.07 5.93 5.37 8.03

Moderate

Support

Initiation Mean 5.77 9.18 18.84 31.61 17.62 15.54 14.80

SD 1.62 4.97 11.16 8.76 16.92 9.56 7.03

Reaching Mean 4.64 5.70 16.00 15.27 12.25 12.41 13.73

SD 1.30 1.40 5.17 3.03 9.87 4.00 4.42

Grasping Mean 4.42 6.22 25.80 16.96 13.94 14.52 18.56

SD 0.73 1.22 9.92 4.97 7.86 3.75 5.87


SD 0.46 1.75 11.17 10.37 0.68 4.98 4.11

Table 14. Healthy subject EMG maximum magnitude (% of MVC)

Level of

support Phase EMG

Ant.

Deltoid

Mid.


Ext.

Digitorum

Flex.


Mild

Support

Initiation Mean 17.33 9.31 3.57 2.36 4.61 1.05 2.31

SD 1.84 1.12 0.74 0.16 0.57 0.17 0.40

Reaching Mean 25.37 13.92 7.18 2.60 6.34 1.38 3.28

SD 2.06 1.48 1.85 0.38 0.96 0.28 0.68

Grasping Mean 26.26 14.26 8.10 2.87 21.72 6.02 17.80

SD 4.88 3.01 1.82 0.36 4.48 1.19 3.98


SD 2.69 1.37 1.38 0.51 3.29 0.96 2.97

Moderate

Support

Initiation Mean 10.96 4.84 0.84 2.84 2.30 0.70 1.40

SD 1.83 0.87 0.30 0.18 0.71 0.10 0.37

Reaching Mean 15.77 9.33 1.82 2.83 3.99 1.09 2.48

SD 1.29 1.48 0.41 0.33 1.29 0.36 1.01

Grasping Mean 14.57 7.98 1.83 2.98 13.18 4.24 11.89

SD 2.57 1.46 0.47 0.46 1.49 0.49 1.58


SD 2.15 1.28 0.47 0.26 1.38 0.54 1.39

38

Table 15. Stroke subject EMG maximum magnitude (% of MVC)

Level of

support Phase EMG

Ant.

Deltoid

Mid.


Ext.

Digitorum

Flex.


Mild

Support

Initiation Mean 20.16 15.78 76.14 17.03 20.45 18.74 28.54

SD 7.02 3.61 25.70 5.24 6.30 3.69 8.17

Reaching Mean 50.69 27.56 98.77 21.69 40.22 30.28 76.48

SD 19.24 8.06 15.70 9.38 10.41 6.14 11.18

Grasping Mean 54.77 38.14 118.98 54.79 79.83 60.28 114.43

SD 21.61 15.67 13.54 29.03 11.16 11.54 14.29


SD 20.00 12.15 13.50 13.91 9.01 10.07 21.16

Moderate

Support

Initiation Mean 6.90 11.33 25.40 40.01 22.24 20.53 17.22

SD 1.50 5.64 11.62 5.80 20.82 11.77 9.37

Reaching Mean 15.13 18.26 47.18 51.06 35.13 29.23 37.16

SD 7.75 5.92 13.21 14.10 25.56 15.69 27.18

Grasping Mean 24.20 32.11 63.03 78.72 73.12 58.44 96.82

SD 10.02 14.13 16.81 17.89 9.86 13.04 21.45


SD 7.32 5.21 13.89 13.20 14.25 4.96 9.03

Table 16. P-values for between-subjects average EMG magnitude

Level of

support Phase

Ant.

Deltoid

Mid.


Ext.

Digitorum

Flex.


Mild

Support

Initiation 0.899 0.001 0.000 0.000 0.000 0.000 0.000

Reaching 0.042 0.006 0.000 0.000 0.000 0.000 0.000

Grasping 0.176 0.019 0.000 0.001 0.000 0.000 0.000

Transporting 0.080 0.024 0.000 0.000 0.000 0.000 0.000

Moderate

Support

Initiation 0.001 0.010 0.001 0.000 0.041 0.008 0.002

Reaching 0.000 0.002 0.000 0.000 0.011 0.001 0.000

Grasping 0.110 0.000 0.000 0.000 0.000 0.000 0.000

Transporting 0.093 0.000 0.000 0.000 0.000 0.000 0.000

Discussion

Our hypothesis was that gravity compensation would influence the movement

performance and muscle activation patterns of stroke patients than the healthy controls.

Knowledge of the nature and direction of these changes will enhance our understanding

39

of underlying working mechanisms of the influence of gravity compensation on

improvements in arm movement ability.

The present case study provided initial experimental data on the effects of

increasing gravity weight-support levels on the upper limb reaching movements using a

gravity-supported, computer-enhanced Armeo®Spring device in stroke survivors. The

results of this study provided evidence that increasing the amount of gravity weight-

support provided to the upper limb has a potential to enhance the ability of stroke

survivors to perform faster, and more smooth reaching movements.

The data showed that the increase in the gravity weight-support levels enabled the

stroke subject to complete the reaching cycle in significantly less time. This significance

is attributed to the significant decrease in the time needed to complete the grasping phase

of the cycle which means that the increase in gravity weight-support level enabled the

subject to perform a more accurate and precise movement to reach for their target (the

apple). Also, this can be supported by the significantly decreased moving velocity found

during the grasping phase, as lower moving velocity is needed in order to execute more

accurate movements. Additionally, the moving velocity during initiation phase of the

cycle was significantly increased when changing the weight-support to a higher level.

This increase may indicate that the device is capable of helping stroke patient to initiate

movements more efficiently which is a barrier that most of stroke survivors face when

they attempt to make goal-directed movements. Based on our knowledge, no studies were

found reporting the effects of increasing the gravity compensation on the task completion

time or the moving velocity of the upper arm.

40

When comparing muscle activity levels during the cycle between two different

support levels, we found that the level of muscle activity was lower during movements

with higher weight-support in the anterior deltoid, middle deltoid, biceps, and

brachioradialis muscles in both the stroke subject and the control subject. In other words,

in both stroke and control subjects, the increase in weight-support level reduced the level

of muscle activity needed to hold the arm in a certain orientation during the cycle. These

results are consistent with findings reported by Jannink et al. (2007) and Prange et al.

(2009). This finding supports that the Armeo®Spring device enable the stroke patients to

generate movements with less efforts. Perry, Powell, and Rosen (2009) reported that the

majority of human arm joint torques are devoted to supporting the human arm position in

space while compensating gravitational loads whereas a minor portion is dedicated to arm

motion itself.

During the reaching cycle, the results showed a decrease in the glenohumeral joint

abduction movements accompanied with an increase in the elbow joint extension

movements in the stroke subject while using a higher weight-support level. These results

suggest that the increase in weight-support level changed the motion control mechanism

to depend more on the movement of the distal joint (elbow) with less contribution from

the proximal joint (glenohumeral) to complete the reaching cycle. This suggestion can

explain the increased EMG magnitude found in triceps muscle after increasing the level

of weight-support. This finding can be supported by a recent research that showed that

the arm support decreases the required shoulder abduction torques during two

dimensional reaching movements at shoulder height, subsequently causing a decrease in

coupled elbow flexion, leading to an increase in the range of elbow extension (Iwamuro,

41

Cruz, Connelly, Fischer, & Kamper, 2008; Krabben et al., 2012). The increase in the

triceps EMG magnitude can be attributed also to force needed to push against the extra

weight support provided by the Armeo®Spring device under the moderate weight-support

condition.

The stroke subject showed larger shoulder adduction movements comparing to the

healthy control, however, these results were difficult to conclude as the moving range

(i.e., working space) was calculated and defined for each subject to allow each individual

to be able to complete the Fruit Shopping task.

There were several limitations of this study. First, we encountered challenges in

study setup as the markers were sometimes obstructed by the Armeo®Spring devices

while subjects were performing the reaching task. We have spent considerable amount of

time to perform data cleaning (fill in gaps, correct switching maker data) to ensure the

quality and validity of the data. Second, we did not randomize the two conditions (mild

and moderate weight-support). Subjects were instructed to complete the Fruit Shopping

under mild weight support followed by moderate weight support. As a result, one could

argue that observed changes under these two conditions might be due to practice or

fatigue effects.

In summary, the significant differences in movement performance between mild

and moderate physical weight support suggest the gravity-supported mechanism provides

a mean to facilitate functional upper limb motor performance in individuals with stroke.

42

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52

Appendix A: Previous Reaching Studies in the Literature

53

AUTHOR

(year)

N AGE

(years)

TIME SINCE

STROKE

RESEARCH DESIGN OUTCOME

MEASURES

RESULTS & CONCLUSION

Cirstea, Ptito,

& Levin

(2003)

30 right hand-

dominant

EG: 19 to 74

years (mean ±

SD:

53.5±16.4

years)

CG: aged

43.3±18.2 years

EG: right

hemiparesis due to

a single leftsided

cerebrovascular

accident (CVA)

that occurred 3–17

months

prior to the study

Figure 1

Between-group

comparison

EG: participants were

divided into two

subgroups

based on the severity of

their motor impairment:

1. SG1: (P1–10)

mild-to-moderate motor

impairment. FM scores

between 63 and 50.

2. SG2: (P11–20)

a moderate (gross and

some fine movement) to

severe (gross motor

function only) motor

impairment. FM scores

between 46 and 5

the arm

subsection of

the FM

CSI

• SG1: practice resulted in an increase

in trunk recruitment (either anterior

displacement or rotation), which

occurred in a situation where such

recruitment was not required for the

task.

• Without particular attention to

compensatory strategies, movement

repetition training results in an

increase in compensatory trunk use in

those patients who tended to use more

trunk displacement before training.

• Motor function may be improved by

repeated practice even in chronic

stroke during a single session of

intensive practice, but that therapy

aimed at functional arm recovery

should consist of a larger number of

movement sessions for motor learning

to occur.

• Task-oriented training improves

movement outcome and performance

in patients with mild-to-moderate

hemiparesis (no need for knowledge of

performance), while motor

performance (i.e. joint motion) might

have to be explicitly addressed (i.e.

54

knowledge of performance provided)

for patients with more severe

impairments.

(Hingtgen,

McGuire,

Wang, &

Harris, 2006)

8 51.37±14.8

years

subjects had

experienced a

stroke and were

scheduled to

receive BOTOXs

(Botulinum Toxin

Type A)

Figure 2

subjects seated in a chair

at a therapy table, and

verbally instructed to

place their hand against

their sternum. Next, the

patients were instructed

to reach as far as they

can at their own pace to

the target directly in

front of them. After

reaching the target, the

subjects end the reaching

cycle with their hand

against their sternum.

kinematic

variables of

movement time,

range of motion,

peak angular

velocity, and

percentage of

reach where

peak velocity

occurs

• An UE kinematic model for motion

analysis is proposed

• The current model provides

calculations of the joint angular

velocity profile of reaching cycles.

• The static and dynamic evaluation

tests confirm the system’s accuracy

and precision in describing 3D upper

extremity motion.

• The current model was useful in

detecting significant differences

between affected and unaffected

metrics (range of motion, peak angular

velocity)

(Kamper,

McKenna-Cole,

Kahn, &

Reinkensmeyer,

2002)

20 EG: 16 age

from 30 to 85

years

CG: 4

Chronic stroke

patients from 9

months to 6 years

and in arm

impairment level

from severe to

mild.

Figure 3

Participants reached

toward a screen of 75

targets spanning an

approximate range from

± 90° side to side and

from waist to head.

Chedoke-

McMaster

Stroke

Arm

Assessment,

distance,

velocity,

smoothness,

straightness,

and direction of

• Reaches performed with the impaired

arms showed significant degradation

in all performance measures. Although

only modestly dependent on the target

location, these features correlated

strongly with impairment level, as

well as with each other. Reaching

distance showed the strongest

correlations with the other parameters.

55

the hand path

during each

reach

• Stroke alters a broad array of features

of reaching, yet largely the same

degree of movement control is

preserved across a range of target

locations. The only consistently

problematic task is to reach far out

from the torso, independent of the

movement direction.

(Leonard,

Gardipee,

Koontz,

Anderson, &

Wilkins, 2006)

13 mean age of

62.8 (SD 9.5)

years

history of stroke

with a diagnosis of

spastic-type

hemiparesis

involving the

upper extremity of

at least 10 months’

duration

Figure 4

Prospective, cross-

sectional, correlation

matrix using sample of

convenience.

subjects were positioned

in an upper extremity

armature for muscle

stiffness, strength and

co-contraction data

collection

during biceps and triceps

brachii MVC trials,

voluntary reaching to a

target and during passive

movements that

mimicked the speed and

trajectory of the

subjects’ voluntary

movements.

FM-UE Motor,

MAS, deep

tendon reflexes,

muscle

stiffness, paresis

and co-

contraction

during a

voluntary

reaching task

and during

passive

movements.

• Paresis of the biceps and triceps

brachii and co-contraction of the

biceps brachii during voluntary

reaching were the impairments most

significantly correlated to motor

performance.

• It would appear that although

increased passive muscle stiffness and

decreased reflex thresholds are indeed

present in individuals with chronic

hemiparesis post stroke, these

impairments do not appear to be the

primary limitations during voluntary,

unperturbed movement to a predicted

target.

56

(Levin, 1996) 16 EG: 10

hemiparetic

mean age was

48.5±9.3 years

CG=6 age/sex

matched

had sustained a

single CVA

leading to upper

limb paresis

Figure 5

subjects were seated in

front of a height-

adjustable table.

Movements started from

the midline of the body

at a distance of -15 cm

from the chest. In the

initial position, the

shoulder was abducted

45°, the elbow was

flexed 45° and the

forearm was pronated so

that the hand rested on

the table. The near and

far targets were placed in

a sagittal direction 200

and 400 mm,

respectively, away from

the initial position. The

ipsilateral and

contralateral targets were

placed 200 mm lateral to

the near target in the

ipsilateral and

contralateral workspace,

respectively

MAS

FM

• for stroke patients having no

perceptuomotor problems (apraxia,

leftsided neglect), movement

disruption occurs at the level of

interjoint coordination and is not

linked to pathological movement

synergies.

• treatment aimed at improving arm

function should be oriented at

restoring the normal sensorimotor

relationships between the joints.

• Once tone has been decreased, patients

should practice coordinated

movements with increasing difficulty

and speed.

• contrary to the traditional belief that

muscle strengthening would only

serve to augment spasticity and

abnormal postural relationships, if

administered at the appropriate time,

specific strengthening of agonist

muscles may be of benefit to the re-

education of movement

(Sainburg &

Kalakanis,

6 24–36 yr of

age.

neurologically

intact, right-

Figure 6 Beckman

Instruments

were used to

• After task adaptation, final position

accuracy was similar for both hands;

however, the hand trajectories and

57

2000) handed adults

subjects sat facing a

computer screen with

each arm supported, over

a horizontal table top at

shoulder height, by a

frictionless air-jet

system. All joints distal

to the elbow were

immobilized using an air

splint. The scapulae and

trunk were immobilized

using a custom-fit

butterfly-shaped chest

restraint.

monitor the

elbow and

shoulder joint

angles.

joint coordination patterns during the

movement were systematically

different. The trajectories of both

hands were not straight but exhibited

oppositely directed curvatures.

• Results show interlimb differences in

the relative timing, magnitude, and

direction of muscle torques at the

shoulder and elbow that are more

likely to result from differences in

neural activation.

Abbreviations: CG - Control Group; CSI: the Composite Spasticity Index; EG - Experimental Group; FM: Fugl-Meyer; MVC:

Maximal Voluntary Contraction; MAS: modified Ashworth scale; UL - Upper limb, UE - Upper extremity

58

Figure 1 Figure 2 Figure 3

Figure 4 Figure 5 Figure 6

59

Appendix B: Text Descriptions

60

Figure 1.

Brief Description: The Armeo®Spring study setup

Summary Description: The study setup is illustrated with the Armeo®Spring device

facing a computer to provide the testing software and a subject wearing the device.

Detailed Description: The study setup includes three main components. First, the

Armeo®Spring device which is a gravity-supporting exoskeleton apparatus that contains

no robotic actuators. It is the commercialized product of Therapy Wilmington Robotic

Exoskeleton (T-WREX) (Housman, Scott, & Reinkensmeyer, 2009) which has been re-

designed by Hocoma, Inc. with user-friendly software and hardware interface to be used

in the routine clinical settings. The main structure of the device consists of an arm

exoskeleton with integrated springs providing a 5 degree-of-freedom movement at the

shoulder, elbow, and wrist levels. It embraces the whole arm, from shoulder to hand, and

counterbalances the weight of the patient’s arm providing a sense of arm flotation at all

positions in the available workspace. The second component is a computer facing the

Armeo®Spring device with its display being set at the level of the subject’s eyes to

provide the testing software for the study. The third component is the subject who is

wearing the Armeo®Spring device while seated on a chair and facing the display of the

computer.

Figure 2.

Brief Description: Armeo®Spring weight support system.

61

Summary Description: The Armeo®Spring device contains two weight support systems at

the upper arm level and the forearm level.

Detailed Description: The main structure of the Armeo®Spring device consists of an arm

exoskeleton with integrated springs providing a 5 degree-of-freedom movement at the

shoulder, elbow, and wrist levels. It embraces the whole arm, from shoulder to hand, and

counterbalances the weight of the patient’s arm providing a sense of arm flotation at all

positions in the available workspace. The upper arm support provided by an integrated

spring contains multiple level of support. These levels are displayed on the device as a

scale from A to K, with A is the minimum level of support and K is the maximum. The

forearm support contains a scale from 1 to 5 displayed on the device with 1 is the

minimum level of support and 5 is the maximum.

Figure 3.

Brief Description: The print screen of the fruit shopping game.

Summary Description: The fruit shopping game is the software that was used for testing

subjects while using the Armeo®Spring device.

Detailed Description: The fruit shopping game is about picking apples and placing them

in a shopping cart. The apples are green in color and will show up one at a time across a

computer screen while the shopping cart is placed at the lower left corner of the screen.

To complete the game, the user should move a hand-like pointer using the Armeo®

Spring

from the initial start position to reach an apple that turns from green to red in color.

When the pointer is over the red apple, the user should squeeze/grasp the pressure

sensitive handgrip of the Armeo®Spring device to hold the apple and transport the apple

62

to the shopping cart. When the color of the cart changes the user should take the pressure

off the device handgrip to release the apple. This process is illustrated in five phases

shown in the figure; the initiation, reaching, grasping, transporting and releasing phase.

Figure 4.

Brief Description: The mean completion time between the two gravity-support levels.

Summary Description: The mean time needed to complete each phase of the reaching

cycle and the total mean duration needed to complete the reaching cycle for a healthy

subject and a stroke subject under the mild and moderate level of support provided by the

Armeo®Spring device is displayed in column graph.

Detailed Description: The mean time needed to complete each phase of the reaching

cycle and the total mean duration needed to complete the reaching cycle for a healthy

subject and a stroke subject under the mild and moderate level of support provided by the

Armeo®Spring device is displayed in column graph. The x-axis represents the four phases

of the reaching cycle (Initiation, Reaching, Grasping, and Transporting) and the total

duration. In each phase and the total duration, four columns are displayed side by side.

The first column is blue in color and represents the healthy subject while using the mild

weight-support. The second column is red in color and represents the healthy subject

while using the moderate level of support. The third one is green in color and represents

the stroke subject while using the mild weight-support and the last column is purple in

color and represents the stroke subject while using the moderate level of weight-support.

The y-axis represents the time in seconds.

63

Figure 5.

Brief Description: Joint angle changes during the reaching cycle for the healthy subject

(upper panel) and the stroke subject (lower panel).

Summary Description: The joint angle changes during the reaching cycle are displayed in

a graph. The upper panel represents the changes in the joint angles for the healthy subject

and the lower panel represents the changes in joint angles for the stroke subject.

Detailed Description: The joint angle changes during the reaching cycle are displayed in

a graph. The graph is divided to two panels, the upper panel represents the changes in the

joint angles for the healthy subject and the lower panel represents the changes in joint

angles for the stroke subject. Each panel displays three line charts sorted vertically. The

top chart represents the changes in abduction/adduction angles of the shoulder joint. The

middle chart represents the changes in flexion angles of the shoulder joint and the lower

chart represents the changes in flexion angles of the elbow joint. In each chart, two lines

are displayed; a blue line which represents the joint angles under the mild weight-support

and a red line which represents the joint angles under the moderate weight-support. The

x-axis in each chart represents the changes in joint angles as a percentage of the reaching

cycle. The y-axis represents the level of change in joint angles in degrees.

Documents

Kinematic and Kinetic Comparisons of Arm and Hand Reaching